Text Classification using Mamba Model
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Updated
Jun 19, 2024 - Python
Text Classification using Mamba Model
Sentiment classification and opinion prediction based on a movie reviews dataset.
A simple movie search engine using IMDB data and ElasticSearch.
A solver for Actorle, the daily actor guessing game
Implementation of Naive Bayes Classifier for the IMDB reviews dataset
Movie Counsel helps you to get tailored Movie/Series recommendation with an inbuilt Sentiment Analyzer Tool for Movie Reviews
"MovieMaven" allows users to search for a movie by its title and displays various details about the movie, such as its rating, genres, plot, directors, cast, writers, production companies, box office gross ,budget and recommendations
Discover the potential of AI with projects showcasing Logistic Regression for classification and an advanced Reversi Algorithm. Explore practical applications and insights into the world of machine learning. 🤖📈🎮
A crawler that gathers detailed information about the top 250 movies on IMDb and stores them in a SQLite database using the PeeWee ORM library.
Explore this deep learning model for sentiment analysis trained on IMDb Movie Reviews data. It uses GloVe word embeddings and LSTM layers to classify text as positive or negative. Perfect for text sentiment tasks!
Python scripts for analyzing the 'Top 250 IMDb TV Shows' dataset. Tasks include EDA, building a TV show recommendation system, sentiment analysis, IMDb rating prediction, and TV show clustering.
GCN model trained on the IMDB-BINARY dataset and a custom graph class to modify graphs in the dataset as well as explain and approximate the model.
🎞️ School Big Data Project
Masked Language Model Task Implementation in Tensorflow2
Tensorflow Model Incorporable Sentencepiece Tokenizer Training Code
Translate movie titles into German.
The Python script provided is an example of web scraping using BeautifulSoup and requests libraries to extract data from IMDb's top-rated movies page. The script sends a GET request to the IMDb website, downloads the HTML content, and parses it using BeautifulSoup.
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